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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/140831


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    題名: Categorical Nature of Major Factor Selection via Information Theoretic Measurements
    作者: 周珮婷
    Chou, Elizabeth P.
    Chen, Ting-Li;Fushing, Hsieh
    貢獻者: 統計系
    關鍵詞: CEDA;conditional entropy;conditional mutual information;heterogeneity;information gain
    日期: 2021.12
    上傳時間: 2022-07-07 11:24:01 (UTC+8)
    摘要: Without assuming any functional or distributional structure, we select collections of major factors embedded within response-versus-covariate (Re-Co) dynamics via selection criteria [C1: confirmable] and [C2: irrepaceable], which are based on information theoretic measurements. The two criteria are constructed based on the computing paradigm called Categorical Exploratory Data Analysis (CEDA) and linked to Wiener–Granger causality. All the information theoretical measurements, including conditional mutual information and entropy, are evaluated through the contingency table platform, which primarily rests on the categorical nature within all involved features of any data types: quantitative or qualitative. Our selection task identifies one chief collection, together with several secondary collections of major factors of various orders underlying the targeted Re-Co dynamics. Each selected collection is checked with algorithmically computed reliability against the finite sample phenomenon, and so is each member’s major factor individually. The developments of our selection protocol are illustrated in detail through two experimental examples: a simple one and a complex one. We then apply this protocol on two data sets pertaining to two somewhat related but distinct pitching dynamics of two pitch types: slider and fastball. In particular, we refer to a specific Major League Baseball (MLB) pitcher and we consider data of multiple seasons.
    關聯: Entropy, Vol.23, No.12, 1684
    資料類型: article
    DOI 連結: https://doi.org/10.3390/e23121684
    DOI: 10.3390/e23121684
    顯示於類別:[統計學系] 期刊論文

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